Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait, ～1,900,000 animals, and ～5,900 genotyped elite artificial insemination rams. For the ～8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare “partial” and “whole” predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the “official” (routine) evaluation, which is a mixture of both single and multiple trait, and “deletion” of data before 1990; and (2) several models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relationship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies estimated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdispersion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gamma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball effect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records.     

Western Canadian dairy farmers' perspectives on the provision of outdoor access for dairy cows and on the perceptions of other stakeholders

The provision of pasture and outdoor access for dairy cattle differs around the globe. For example, in Ireland, New Zealand, and Australia, dairy farms are largely pasture based, whereas dairy farms in the United States and Canada are largely confinement based. There is a high level of public support for pasture and outdoor access for dairy cows, and the available evidence shows that dairy cattle are highly motivated to access pasture, especially at night. The decision as to whether to provide outdoor access is typically made by farmers, but little is known about dairy farmers' perspectives on this topic. We investigated perspectives of Western Canadian dairy farmers on outdoor access, as well as how they believe different stakeholders (i.e., the dairy industry, the dairy cows, and the general public) regard outdoor access for dairy cows. Data were collected via (1) 11 focus group discussions with a total of 50 Western Canadian dairy farmers, and (2) semi-structured individual interviews with an additional 6 dairy farmers of Hutterite colonies. Data were analyzed using template analysis. Although most participants in this study did not provide outdoor access on their farms, or only provided outdoor access to certain cow groups, participants generally mentioned that they enjoyed seeing cows on pasture or outdoors. However, participants shared that the Canadian supply management system (including processors) required a consistent flow of production, which was thought to be easier and more economically realized with indoor housing of lactating cows. Participants believed that pasture or outdoor access for dairy cows was desired by the public. Some participants believed that dairy cows prefer to spend time outside under favorable weather conditions, but others felt that cows preferred to stay indoors in modern, ventilated freestall barns. The results of this study describe the perspectives of dairy farmers regarding the views of dairy industry stakeholders as they relate to outdoor access, helping to inform conversations around the provision of outdoor access for dairy cattle.     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

基于多媒体技术的思想政治教育课程资源整合系统

为解决资源种类划分结果异常,设计基于多媒体技术的思想政治教育课程资源整合系统。选用CYUSB3014芯片作为此次设计系统的主控芯片,并设定信息处理芯片以及外围电路。在此基础上,对思政课程信息进行分类,建立了课程资源库。优化资源整合模块,设定多媒体资源整合流程,并添加DataSet与.NET Framework数据处理程序,实现对多媒体数据的高效处理。构建系统测试环节显示,此系统资源种类划分结果较为合理且系统承载率较高。由此可知,此系统在应用中具有较高的综合性能。     

Changes in polyphenol fractions and bacterial composition after in vitrofermentation of apple peel polyphenol by gut microbiota

The changes in polyphenol fractions after in vitro fermentation of apple peel polyphenol (APP) by gut microbiota as well as the effects of APP on the growth, pH value, short-chain fatty acids (SCFAs) production and intestinal flora composition of gut microbiota fermentation were firstly explored in this study. The relative abundance of Lactobacillus in the APP group was 49.55% with an increment of 49.40% compared with the blank group, while that of Bifidobacterium was 13.32%. Moreover, the flora produced 37.093 ± 0.478 mM of SCFAs including acetic acid, propionic acid, butyric acid and valeric acid during the process of fermentation, thus reduced the environmental pH value. In addition, polyphenol fractions in APP were altered by gut microbiota fermentation to some extent, for example, glycosides were hydrolysed to aglycones. These findings suggested APP as a potential prebiotic agent to alleviate the disorder of intestinal flora.     

Winter diets of round whitefish Prosopium cylindraceum in the St. Marys River

The round whitefish (Prosopium cylindraceum) is a coregonine native to numerous northern temperate lentic and lotic systems across North America. The St. Marys River, located between the United States and Canadian border, is one such system where the round whitefish supports a recreational fishery and is a prey source for piscivores. Round whitefish are generally known to prey upon benthic macroinvertebrates, but diets of round whitefish in the St. Marys River have not been characterized. We analyzed stomach contents from 107 round whitefish sampled from the St. Marys River during the winter months of 2020 and 2021 to describe their diets, examine their feeding strategies, and determine if they fed selectively. Ephemeropterans were the most important prey numerically, followed by megalopterans, trichopterans, and dipterans. The round whitefish population sampled from the St. Marys River exhibited a specialized feeding strategy that most often specialized on ephemeropterans. Patterns in the importance of prey types and feeding strategy were consistent between both years sampled. Selective feeding by round whitefish was examined using diet and environmental data from 2021 only, and we found that round whitefish selected both ephemeropterans and megalopterans, but avoided trichopterans and dipterans. Rather than being opportunistic invertivores, our results indicate round whitefish are specialized and selective feeders. Our findings can be used to inform conservation and management of round whitefish, as diets represent an integration of multiple ecological components such as prey availability, predator behavior, and habitat quality.     

Development of a stakeholder identification and analysis method for human factors integration in work system design interventions – Change Agent Infrastructure

In any work system design intervention—for example, a physical workplace re-design, a work process change, or an equipment upgrade—it is often emphasized how important it is to involve stakeholders in the process of analysis and design, to gain their perspectives as input to the development, and ensure their future acceptance of the solution. While the users of an artifact or workplace are most often regarded as being the most important stakeholders in a design intervention, in a work-system context there may be additional influential stakeholders who influence and negotiate the design intervention's outcomes, resource allocation, requirements, and implementation. Literature shows that it is uncommon for empirical ergonomics and human factors (EHF) research to apply and report the use of any structured stakeholder identification method at all, leading to ad-hoc selections of whom to consider important. Conversely, other research fields offer a plethora of stakeholder identification and analysis methods, few of which seem to have been adopted in the EHF context. This article presents the development of a structured method for identification, classification, and qualitative analysis of stakeholders in EHF-related work system design intervention. It describes the method's EHF-related theoretical underpinnings, lessons learned from four use cases, and the incremental development of the method that has resulted in the current method procedure and visualization aids. The method, called Change Agent Infrastructure (abbreviated CHAI), has a mainly macroergonomic purpose, set on increasing the understanding of sociotechnical interactions that create the conditions for work system design intervention, and facilitating participative efforts.